2023 IEEE International Conference on Quantum Software (QSW)最新文献_第3页

Recommending Solution Paths for Solving Optimization Problems with Quantum Computing 推荐用量子计算解决优化问题的解决路径

2023 IEEE International Conference on Quantum Software (QSW) Pub Date : 2022-12-21 DOI: 10.1109/QSW59989.2023.00017

Benedikt Poggel, Nils Quetschlich, Lukas Burgholzer, R. Wille, J. Lorenz

{"title":"Recommending Solution Paths for Solving Optimization Problems with Quantum Computing","authors":"Benedikt Poggel, Nils Quetschlich, Lukas Burgholzer, R. Wille, J. Lorenz","doi":"10.1109/QSW59989.2023.00017","DOIUrl":"https://doi.org/10.1109/QSW59989.2023.00017","url":null,"abstract":"Solving real-world optimization problems with quantum computing requires choosing between a large number of options concerning formulation, encoding, algorithm and hardware. Finding good solution paths is challenging for end users and researchers alike. We propose a framework designed to identify and recommend the best-suited solution paths in an automated way. This introduces a novel abstraction layer that is required to make quantum-computing-assisted solution techniques accessible to end users without requiring a deeper knowledge of quantum technologies. State-of-the-art hybrid algorithms, encoding and decomposition techniques can be integrated in a modular manner and evaluated using problem-specific performance metrics. Equally, tools for the graphical analysis of variational quantum algorithms are developed. Classical, fault tolerant quantum and quantum-inspired methods can be included as well to ensure a fair comparison resulting in useful solution paths. We demonstrate and validate our approach on a selected set of options and illustrate its application on the capacitated vehicle routing problem (CVRP). We also identify crucial requirements and the major design challenges for the proposed abstraction layer within a quantum-assisted solution workflow for optimization problems.","PeriodicalId":254476,"journal":{"name":"2023 IEEE International Conference on Quantum Software (QSW)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129476397","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3

Agile Practices for Quantum Software Development: Practitioners’ Perspectives 量子软件开发的敏捷实践:实践者的视角

2023 IEEE International Conference on Quantum Software (QSW) Pub Date : 2022-10-18 DOI: 10.1109/QSW59989.2023.00012

A. Khan, M. Akbar, Aakash Ahmad, M. Fahmideh, Mohammad Shameem, V. Lahtinen, M. Waseem, T. Mikkonen

{"title":"Agile Practices for Quantum Software Development: Practitioners’ Perspectives","authors":"A. Khan, M. Akbar, Aakash Ahmad, M. Fahmideh, Mohammad Shameem, V. Lahtinen, M. Waseem, T. Mikkonen","doi":"10.1109/QSW59989.2023.00012","DOIUrl":"https://doi.org/10.1109/QSW59989.2023.00012","url":null,"abstract":"Quantum software engineering is an emerging genre of software engineering that exploit principles of quantum bits (Qubit) and quantum gates (Qgates) to solve complex computing problems effeciently than their classical counterparts. According to its proponents, agile software development practices have the potential to address many of the problems endemic to the development of quantum software. However, there is a dearth of evidence investigating whether agile practices are suitable for, and can be adopted by, software teams in the context of quantum software development. To address this lack, we conducted an empirical study to investigate the needs and challenges of using agile practices to develop quantum software. While our semi-structured interviews with 26 practitioners across 10 countries highlighted the applicability of agile practices in this domain, the interview findings also revealed new challenges impeding the effective incorporation of these practices. Our research findings provide a springboard for further contextualization and seamless integration of agile practices in quantum software engineering (QSE) to develop emerging and next generation of quantum software systems and application.","PeriodicalId":254476,"journal":{"name":"2023 IEEE International Conference on Quantum Software (QSW)","volume":"69 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126315275","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 6

Predicting Good Quantum Circuit Compilation Options 预测好的量子电路编译选项

2023 IEEE International Conference on Quantum Software (QSW) Pub Date : 2022-10-14 DOI: 10.1109/QSW59989.2023.00015

Nils Quetschlich, Lukas Burgholzer, R. Wille

{"title":"Predicting Good Quantum Circuit Compilation Options","authors":"Nils Quetschlich, Lukas Burgholzer, R. Wille","doi":"10.1109/QSW59989.2023.00015","DOIUrl":"https://doi.org/10.1109/QSW59989.2023.00015","url":null,"abstract":"Any potential application of quantum computing, once encoded as a quantum circuit, needs to be compiled in order to be executed on a quantum computer. Deciding which qubit technology, which device, which compiler, and which corresponding settings are best for the considered problem—according to a measure of goodness—requires expert knowledge and is overwhelming for end-users from different domains trying to use quantum computing to their advantage. In this work, we treat the problem as a statistical classification task and explore the utilization of supervised machine learning techniques to optimize the compilation of quantum circuits. Based on that, we propose a framework that, given a quantum circuit, predicts the best combination of these options and, therefore, automatically makes these decisions for end-users. Experimental evaluations show that, considering a prototypical setting with 3000 quantum circuits, the proposed framework yields promising results: for more than three quarters of all unseen test circuits, the best combination of compilation options is determined. Moreover, for more than 95% of the circuits, a combination of compilation options within the top-three is determined—while the median compilation time is reduced by more than one order of magnitude. Furthermore, the resulting methodology not only provides end-users with a prediction of the best compilation options, but also provides means to extract explicit knowledge from the machine learning technique. This knowledge helps in two ways: it lays the foundation for further applications of machine learning in this domain and, also, allows one to quickly verify whether a machine learning algorithm is reasonably trained. The corresponding framework and the pre-trained classifier are publicly available on GitHub (https://github.com/cda-tum/MQTPredictor) as part of the Munich Quantum Toolkit (MQT).","PeriodicalId":254476,"journal":{"name":"2023 IEEE International Conference on Quantum Software (QSW)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129143182","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 13

LAWS: Look Around and Warm-Start Natural Gradient Descent for Quantum Neural Networks 定律:量子神经网络的环顾四周和热启动自然梯度下降

2023 IEEE International Conference on Quantum Software (QSW) Pub Date : 2022-05-05 DOI: 10.1109/QSW59989.2023.00019

Zeyi Tao, Jindi Wu, Qi Xia, Qun Li

{"title":"LAWS: Look Around and Warm-Start Natural Gradient Descent for Quantum Neural Networks","authors":"Zeyi Tao, Jindi Wu, Qi Xia, Qun Li","doi":"10.1109/QSW59989.2023.00019","DOIUrl":"https://doi.org/10.1109/QSW59989.2023.00019","url":null,"abstract":"Variational quantum algorithms (VQAs) have recently received much attention due to their promising performance in Noisy Intermediate-Scale Quantum computers (NISQ). However, VQAs run on parameterized quantum circuits (PQC) with randomly initialized parameters are characterized by barren plateaus (BP) where the gradient vanishes exponentially in the number of qubits. In this paper, we proposed a Look Around Warm-Start (LAWS) quantum natural gradient (QNG) algorithm to mitigate the widespread existing BP issues. LAWS is a combinatorial optimization strategy taking advantage of model parameter initialization and fast convergence of QNG. LAWS repeatedly reinitializes parameter search space for the next iteration parameter update. The reinitialized parameter search space is carefully chosen by sampling the gradient close to the current optimal. Moreover, we present a unified framework (WS-SGD) for integrating parameter initialization techniques into the optimizer. We provide the convergence proof of the proposed framework for both convex and non-convex objective functions based on Polyak-Lojasiewicz (PL) condition. Our experiment results show that the proposed algorithm could mitigate the BP and have better generalization ability in quantum classification problems.","PeriodicalId":254476,"journal":{"name":"2023 IEEE International Conference on Quantum Software (QSW)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122731530","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 4